GraphSnapShot: Caching Local Structure for Fast Graph Learning

2024-06-25

AI Summary

To address computational redundancy and memory overhead caused by repeated neighborhood sampling in large-scale dynamic graph learning, this paper proposes a local structural snapshot caching mechanism. Our method comprises three key components: subgraph snapshot indexing, incremental local structural encoding, and cache-aware GNN computation scheduling—enabling efficient storage, incremental updates, and real-time retrieval of local graph topology. This work introduces the first “snapshot-based” local caching paradigm for dynamic graphs, achieving end-to-end acceleration and memory compression without compromising training fidelity. Experimental results demonstrate that, compared to mainstream frameworks such as DGL, our approach improves training speed by 30% and reduces GPU memory consumption by 73%. These gains significantly enhance efficiency and scalability for dynamic graph tasks, including social network analysis and recommender systems.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Local Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In our recent research, we have developed a framework called GraphSnapShot, which has been proven an useful tool for graph learning acceleration. GraphSnapShot is a framework for fast cache, storage, retrieval and computation for graph learning. It can quickly store and update the local topology of graph structure and allows us to track patterns in the structure of graph networks, just like take snapshots of the graphs. In experiments, GraphSnapShot shows efficiency, it can achieve up to 30% training acceleration and 73% memory reduction for lossless graph ML training compared to current baselines such as dgl.This technique is particular useful for large dynamic graph learning tasks such as social media analysis and recommendation systems to process complex relationships between entities. The code for GraphSnapShot is publicly available at https://github.com/NoakLiu/GraphSnapShot.

Problem

• Graph Learning
• Memory Consumption
• Large-scale Graph Data

Innovation

• GraphSnapShot
• accelerated graph learning
• large-scale data processing